setwd("C:/Users/iris9/BioTrajX")
Warning: The working directory was changed to C:/Users/iris9/BioTrajX inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
devtools::load_all()
ℹ Loading BioTrajX
library(Seurat)
seu <- readRDS("C:/Users/iris9/Desktop/GSE164690.all.CD8posT.wScores_pseudotime.rds")
expr <- as.matrix(GetAssayData(seu, layer = "data"))
t<- seu$monocle3_pseudotime
naive<- c("CCR7","TCF7","IL7R")
term<- c('PDCD1','LAG3','HAVCR2','TOX','TIGIT','CTLA4','CXCL13','ENTPD1','ITGAE')
cluster <- seu$functional.cluster
DimPlot(seu, group.by = "functional.cluster")

FeaturePlot(seu, features="monocle3_pseudotime")

FeaturePlot(seu, features="slingshot_pseudotime")

FeaturePlot(seu, features="cytotrace_score")

D <- metrics_D(expr, naive_markers = naive, terminal_markers = term, seu$monocle3_pseudotime)
plot_metrics_D(seu$monocle3_pseudotime,D)

O <- metrics_O(expr, naive_markers = naive, terminal_markers = term, seu$monocle3_pseudotime)
plot_metrics_O(expr,pseudotime = seu$monocle3_pseudotime, O ,naive_markers = naive, terminal_markers = term)

P <- metrics_P(expr,
seu$monocle3_pseudotime,
pathways = c("hsa04660","hsa04640"),
naive_markers = naive,
terminal_markers = term,
id_type = c("SYMBOL","ENSEMBL","ENTREZID"),
species = "Homo sapiens",
assay = NULL,
slot = "data",
min_remaining = 10,
min_fraction = 0.20,
min_genes_per_module = 3,
verbose = TRUE)
plot_metrics_P(expr,seu$monocle3_pseudotime, P)

E <- metrics_E(seu$monocle3_pseudotime,
naive_marker_scores = D$s_i_naive,
terminal_marker_scores = D$s_i_term,
method = c("gmm"))

plot_metrics_E(E)

compute_DOPE_single(seu,
seu$cytotrace_score,
naive_markers = naive,
terminal_markers = term,
pathways = c("hsa04660","hsa04640"))





compute_DOPE_single(seu,
seu$monocle3_pseudotime,
naive_markers = naive,
terminal_markers = term,
pathways = c("hsa04660","hsa04640"))





res_single <- compute_single_DOPE_linear(seu,
seu$slingshot_pseudotime,
naive_markers = naive,
terminal_markers = term,
pathways = c("hsa04660","hsa04640"))





pseudotime_methods <- list(
"monocle3" = seu$monocle3_pseudotime,
"slingshot" = seu$slingshot_pseudotime,
"CytoTRACE" = seu$cytotrace_score
)
res<- compute_multi_DOPE_linear(expr,
pseudotime_list = pseudotime_methods,
naive_markers = naive,
terminal_markers =term,
pathways = c("hsa04660","hsa04640"))
Computing DOPE metrics for 3 trajectories...
==========================================================
--- Processing trajectory: monocle3 ---



--- Processing trajectory: slingshot ---






--- Processing trajectory: CytoTRACE ---





==========================================================
Multi-trajectory DOPE analysis complete!
Best trajectory: slingshot (DOPE score: 0.687)

plot.multi_dope_results(res, type = "bar")


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